Entity Relationship Inference Engine for Sensor Data Analysis
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Solution Overview
Problem
Conventional approaches fail to adequately capture or infer relationships between entities in sensor data, leading to ineffective data analysis and decision-making.
Innovation Solution
A system that processes sensor data to infer hierarchical and geospatial relationships between entities based on their attributes, validates these relationships by ensuring compatibility with location characteristics, and updates the data accordingly, using an inference engine to detect and predict entity movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional approaches are used to track sensor data, then data collection capability is maintained, but relationship inference between entities is insufficient
Solution Approach 1:
The patent introduces an inference engine as an intermediary component that processes sensor data to derive relationships between entities. This mediator layer transforms raw tracking data into meaningful relationship information, resolving the contradiction by adding a dedicated processing layer that recovers relationship information without requiring complete redesign of the entire system architecture.
Solution Approach 2:
The system segments the data processing function into distinct modules: data collection, relationship inference, validation, and updating. By dividing the processing task into separate functional segments, the system can handle relationship inference as a specialized operation, improving information recovery while managing overall complexity through modular architecture.
2Measurement precision
If more comprehensive relationship inference is implemented, then data analysis accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-defining validation criteria and relationship templates before processing sensor data. This allows the inference engine to quickly match observed patterns against pre-established frameworks, improving accuracy while reducing processing time compared to analyzing every possible relationship from scratch.
Solution Approach 2:
The patent implements a feedback mechanism where inferred relationships are validated against location characteristics and sensor data consistency. This iterative validation process ensures high accuracy by correcting inference errors, while the feedback loop optimizes processing efficiency by focusing computational resources on high-confidence inferences rather than exhaustively checking all possibilities.
3Reliability
If relationship validation based on location characteristics is added, then reliability of inferred relationships improves, but system complexity increases
Solution Approach 1:
The validation mechanism is designed with universal applicability, using a single framework to validate multiple types of relationships against various location characteristics. This multi-functional validation system improves reliability across different inference scenarios while avoiding the complexity of separate validation mechanisms for each relationship type.
Solution Approach 2:
The system performs self-validation by automatically checking inferred relationships against location characteristics and sensor data consistency without requiring external intervention. This self-service validation approach improves reliability through automated verification while minimizing the complexity increase by eliminating the need for manual validation protocols and reducing operational overhead.
4Speed
If continuous monitoring and updating of relationships is implemented, then responsiveness to changes improves, but computational resources consume more
Solution Approach 1:
The system implements periodic monitoring and updating of relationships rather than continuous processing. By evaluating relationships at specific intervals and only when sensor data changes are detected, the system maintains responsive updates to relationship changes while significantly reducing computational resource consumption compared to continuous analysis.
Solution Approach 2:
The validation and updating process dynamically adjusts its intensity based on data changes. When sensor data remains stable, the system uses lighter validation protocols; when changes are detected, it activates more comprehensive validation. This dynamic approach enables responsive relationship updates while optimizing computational resource consumption according to actual data volatility.
Data Source
AI summary
Systems and methods are provided for obtaining sensor data comprising one or more entities and one or more attributes of the respective one or more entities. The systems and methods may be configured to infer one or more relationships between the respective one or more entities based on the one or more attributes, and update the sensor data based on the inferred one or more relationships.